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Record W1965552267 · doi:10.1021/bm700745d

Heat-Induced Transfer of Protons from Chitosan to Glycerol Phosphate Produces Chitosan Precipitation and Gelation

2008· article· en· W1965552267 on OpenAlexaff
Marc Lavertu, Dominic Filion, Michael D. Buschmann

Bibliographic record

VenueBiomacromolecules · 2008
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsChitosanChemistryMonomerPolyelectrolytePrecipitationProtonationChemical engineeringPhosphateGlucosaminePolymer chemistryChromatographyPolymerOrganic chemistryIon

Abstract

fetched live from OpenAlex

Recently, chitosan dissolved in solutions containing glycerol phosphate (GP) were found to undergo a sol-gel transition when heated and the proposed gelling mechanism was based on increasing hydrophobic interactions with temperature. Subsequently, an investigation of ionization and precipitation behavior of chitosan, including dependencies on temperature, added salt, and fraction of deacetylated monomers (fD) was performed. This latter study revealed important differences in the temperature dependence of pKa of chitosan versus GP and led us to propose an alternative hypothesis for the mechanism of gelation in chitosan-GP systems whereby heat induces transfer of protons from chitosan to glycerol phosphate thereby neutralizing chitosan and allowing attractive interchain forces to form a physical gel. To investigate this specific molecular thermogelling mechanism, temperature ramp experiments on dilute chitosan-GP solutions were performed. Chitosans with fD of 0.72 and 0.98 were used to prepare solutions with a range of molar ratios of GP to chitosan glucosamine monomer of 1.25 to 10 and with 0 or 150 mM added monovalent salt. Light transmittance measurements were performed simultaneously to indicate precipitation in these dilute systems as a surrogate for gelation in concentrated systems. Measured temperatures of precipitation ranged from 15 to 85 degrees C, where solutions with less GP (used in a disodium salt form) had lower precipitation temperatures. A theoretical model using acid-base equilibria with temperature dependent pKa's, including the electrostatic contribution from the polyelectrolyte nature of chitosan, was used to calculate the degree ionization of chitosan (alpha, the fraction of protonated glucosamine monomer) as a function of temperature and showed a significant decrease in alpha with increased temperature due to proton transfer from chitosan to GP. This heat-induced proton transfer from chitosan to GP was experimentally confirmed by 31P NMR measurements during temperature ramp experiments since the chemical shift of 31P of GP is an indicator of its level of protonation. By assuming average temperature independent values of alpha p that were calculated from measured T(p), the model was able to accurately predict measured temperatures of precipitation (T(p)) of all chitosan-GP mixtures. The resulting alpha(p) were temperature independent but increased with increased chitosan fD and with increased salt. Measurements and theory revealed that T(p) can be adjusted in a predictable manner by changing the chitosan-GP molar ratio and thereby systematically tailored to obtain a large range of precipitation temperatures. Finally, similar temperature ramp experiments using inorganic phosphate and MES in place of GP demonstrated that the temperature-induced precipitation of chitosan also occurs with these buffers, confirming that the key feature of the buffer used with chitosan is its ability to absorb heat-stimulated release of chitosan protons and facilitate chitosan neutralization. A theoretical expression for the variation of chitosan ionization degree with temperature in a system composed of two titratable species (chitosan and buffer) was derived and allowed us to establish the required characteristics of the buffer for efficient heat-stimulated proton transfer between a chitosan and the buffer. These results provide a useful explanation for the mechanism of heat-induced gelation of chitosan-based systems that could be exploited for numerous practical applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.227
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations123
Published2008
Admission routes1
Has abstractyes

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